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2 Safe Harbor Statement The following is intended to outline our general product direction. It is intended for information purposes only, and may not be incorporated into any contract. It is not a commitment to deliver any material, code, or functionality, and should not be relied upon in making purchasing decisions. The development, release, and timing of any features or functionality described for Oracle s products remains at the sole discretion of Oracle. Oracle Confidential Internal/Restricted/Highly Restricted 2

3 Welcome Eva Pierangeli BUSINESS ANALYTICS Universita ROMA TRE 21 Maggio 2015 Oracle Confidential Internal/Restricted/Highly Restricted 3

4 WHY SHOULD YOU CARE ABOUT THAT? Oracle Confidential Internal/Restricted/Highly Restricted 4

5 ORACLE: A GREAT OPPORTUNITY $38.5B in revenue on a trailing twelvemonth basis * 400,000 customers in 145 countries $34B in R&D since 2004 $60B on more than 100 acquisitions More than 25,000 partners More than 120,000 employees 18,000 customer support specialists, speaking 29 languages 17,000 implementation consultants 2.2 million students supported annually YoGePro! Oracle Confidential Internal/Restricted/Highly Restricted 5

6 YOUNG GENERATION PROGRAMME ORACLE ITALIA BA Fly higher and with freedom, be creative and achieve this innovative opportunity together with us Oracle Confidential Internal/Restricted/Highly Restricted 6

7 JOIN YoGePro! A: Cc: Oracle Confidential Internal/Restricted/Highly Restricted 7

8 AGENDA 1 Sviluppo IoT un approccio da Nerd ad Impresa Nino Guarnacci 2 Big Data: success stories with Oracle Rossella Rossi 3 Oracle Big Data Discovery Alessandro Moro Thanks &...! Oracle Confidential Internal

9 Sviluppo Internet of Things: Un approccio da Nerd ad Impresa Nino Guarnacci TECHNOLOGY Universita ROMA TRE 21 Maggio 2015 Oracle Confidential Internal/Restricted/Highly Restricted 9

10 let() develop() listen() exec() managed funded simplicity renewable duplicate() comment() powered push() update() empathetic copy() run() Probably NERD Focused on Verbs refactor() commit() deploy() reverse() debug() add() detect() submit() invoke() test() compile() efficient valuable Handsome Businessman uniqueness revolutionary Black-Belt Adjectives resolutive effective sustainable enabler innovative cost-saving

11 % new jobs created by startups that have less than 5 years 2010 Didi Dache Source: Kauffman Foundation

12 Driving New IoT Services Better Customer Experience New Business Models Operational Improvements Increased Efficiency Grow Revenue Shareholder Value Reduce Cost Preventative Maintenance Remote Diagnostics Safety Systems V2X and C2X Fleet and Use Management HVAC Lighting Safety and Security Resource Utilization Maintenance

13 Industrial Manufacturing Supplier, Warehouse In Transit In Storage Shop Floor Consumer Goods Manufacturing Distribution Customer Oil & Gas Upstream Extraction Processing Downstream Distribution 13

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15 IoT standards competing (and many more) Thread Thread is a networking protocol with security and low-power features that make it better for connecting household devices than other technologies such as Wifi, NFC, Bluetooth or ZigBee. Chris Boross, Nest product manager HomeKit HomeKit is a framework in ios 8 for communicating with and controlling connected accessories where you can create actions to control those devices. Apple developer portal Zwave / ZigBee Z-Wave is a wireless communications protocol designed for remote control, use technology with low-power RF radio embedded or retrofitted into electronic devices and systems. Wikipedia 15

16 Two different approaches Adapt, Integrate and Connect to devices Separate Business Logic from network and aggregation 16

17 MVC is used from most successful language frameworks for: Facilitate reuse of logic separation of concerns less coupling between layers

18 Immediate advantage for applications: Modular Scalable Easy to maintain and grow

19 Gateways Devices Reuse the same concept to: Simplify Devices and Gateways Integration Standardize Business Logic Integrate with Enterprise Backend in IoT landscape

20 Different Standards Same Logic Rapid Integration Fast Growing Simple Upgrading Direct Deploy Bit Debugging Oracle Confidential Internal/Restricted/Highly Restricted 20

21 M like Business Logic Oracle Confidential Internal/Restricted/Highly Restricted 21

22 Building Blocks for IoT Cloud IoT & Cloud enable enterprise faster and at lower risk: Enabling connectivity for a devices directly or through 3 rd party clouds or via gateways less coupling between the layers Oracle Confidential Internal/Restricted/Highly Restricted 22

23 Building Blocks for IoT Cloud IoT Cloud Service MAF OEP Oracle Confidential Internal/Restricted/Highly Restricted 23

24 Oracle Internet of Things Layering Cloud&or&On&Premise& Cloud&or&On&Premise& Cloud&or&On&Premise& Financials( SCM( Financials( SCM( HCM( HCM(

25 Oracle Internet of Things Acquire Analyze Integrate Data to Fuel the Services Value From Services Any device Standards based Intelligence at the edge Streaming data analysis Big data analysis Event driven actions Business process integration Data and enterprise applications

26 Oracle IoT: Verizon Telematics Modular platform for over 50 Telematics features (vehicle, driver ) Full service end-to-end solution: Remote activation and software update with wireless provisioning Data Analytics and Reporting Integration with customers application platforms Exposes features via API gateway: New features added by customers 26

27 Full Service End to End Modular platform for over 50 Telematics features (vehicle, driver ) Collected information by the in-vehicle telematics systems Different devices with unique data needs for rapid expansion x00 K Connected services to Hundreds of Thousands of vehicles Enables usage-based insurance packages on actual driving habits Oracle Confidential Internal/Restricted/Highly Restricted 27

28 Data Comm. Module App Engine Communications Gateway Comm. Infrastructure Provisioning Check Oracle IoT: Verizon Telematics PSAP Content/Service Provider OSS Functions BSS Functions Internet/ MPLS Device IoT Cloud Web Interface Interface Module Oracle CCA Oracle Siebel MKT Routing Engine Oracle SOA Suite ODI, Service Bus Oracle Fusion Middleware Content Management Oracle ERP Oracle BI Oracle BRM Oracle Siebel CRM MPLS MPLS OEM Proprietary Data Customer Data CTI / Service / Contents Application E-Call CSR B-Call CSR Call Centers I-Call CSR Oracle Confidential Internal/Restricted/Highly Restricted 28

29 Oracle IoT: Nike Wearable FuelBand relies on Oracle to Monitor Fitness of 8 million people Monitors active lifestyle of 8 million users on a daily basis Current data grid volume is approximately 150,000 request per minute with about 40 Million objects at any given time on the grid Oracle Coherence & Oracle Exadata for new digital sports platform 29

30 Full Service End to End Modular platform for over 50 Telematics features (vehicle, driver ) Shared platform that would drive a consumer-centric model for the company Expect 5X increase in users, need data to be analyzed and acted upon in real-time 40M 150,000 request per minute with about 40 million objects at any given time on the grid and 30,000 concurrent users Users digital footprint of their activities be able to compare, compete and collaborate their athletic data over time Oracle Confidential Internal/Restricted/Highly Restricted 30

31 Internet of Things Changes Everything FDA (USA) and EU CE approved ingestible and wearable sensors. Delivers precise information about: Medication Ingestion Dose timing and associated physiologic response of patients Heart Rate Activity & Rest Skin Temperature.

32 Cotral Modernize bus services today to handle future demand M2M communications help avoid crippling vehicle and traffic issues Created a blueprint for intelligent transportation systems

33 Let DIY MAF OEP Oracle Confidential Internal/Restricted/Highly Restricted 33

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35 Big Data: success stories with Oracle Rossella Rossi BUSINESS ANALYTICS Universita ROMA TRE 21 Maggio 2015 Oracle Confidential Internal/Restricted/Highly Restricted 35

36 The real drivers for Big Data solution Data Volumes? Cost efficiency Data Variety? Brand reputation Business values Data Velocity?

37 Oracle Big Data success stories. BUSINESS EFFICIENCY. CUSTOMER / CITIZEN EXPERIENCE. INVESTIGATION & RISK ANALYSIS

38 Oracle Big Data success stories Business Efficiency Oracle Confidential Internal

39 CHALLENGE VELOCIZZARE IL PROCESSO DECISIONALE NELLA GESTIONE DI SITUAZIONI CRITICHE Prima Conoscere in tempo reale disponibilita e competenze di personale, equipaggiamenti, dislocazione, stato di servizio,... Force Validation Committee. 40 ufficiali full-time azioni giornaliere. 20+ database da analizzare e integrare. Numerosita di risorse eccezionali (3,500 IT systems - 10,000+ unita operative - 1,1ml soldati, - centinaia campi addestramento, ecc.) Mancanza di integrazione tra i sistemi da accedere e con fonti dati non strutturate. Mancanza di visibilita globale sull intera forza operativa Qualita e integrita dei dati dubbia

40 Dopo Enterprise Management Decision Support

41 We live in a world of near real-time most of the time.. We have to be able to answer questions that were previously beyond our reach We have grown to understand our data and the ability to utilize big data.. The chaos is becoming order Washington, 24 Ottobre 2013 Lt. Colonel Bobby Saxon, Division Chief and Program Director - Army EMDS Program

42 Oracle Big Data success stories. BUSINESS EFFICIENCY. CUSTOMER / CITIZEN EXPERIENCE. INVESTIGATION & RISK ANALYSIS

43 Oracle Big Data success stories Customer/Citizen Experience Oracle Confidential Internal

44 CHALLENGE Rinnovare e migliorare la Customer Experience, attraverso l accelerazione del processo di Gestione Reclami Migliorare la Brand Reputation, danneggiata anche dall alto numero dei reclami (1.8 milioni di reclami nel 2011) Ridurre il Costo di gestione del singolo reclamo (complaints in FSA* league table): valore medio escalation FSA pari a 500 * FSA: Financial Ombudsman Services

45 Ambienti frammentati Diversita tecnologiche Uso massimo di Excel Solo sorgenti tradizionali Unstructured Data Websites Content Systems, Files, Social Media

46 Benefits and Business values aggiungere Modernizzazione (NO Excel) Riduzione tempi e costi di gestione singolo reclamo (da 500 a 70) Interfaccia facile anche per utenti non tecnici Nuove viste su dati piu qualitativi; maggior capacita investigativa Integrazione dati da Social Media (apprezzamento Social) Maggior tempo a disposizione degli utenti per attivita di prevenzione e risoluzione Copyright 2011 Oracle and it's affiliates. All rights reserved.

47 Oracle Big Data success stories. BUSINESS EFFICIENCY. CUSTOMER / CITIZEN EXPERIENCE. INVESTIGATION & RISK ANALYSIS

48 Oracle Big Data Stories Investigation and risk analysis Oracle Confidential Internal

49 CHALLENGE MIGLIORARE PROCESSO VISITE ISPETTIVE monitoring sistemi di gestione Fixed Antenna Network e Mobile Network (Detective Analytics) Risk Prima Costs Prepare Inspect Intervention Expedite

50 Prepare Inspect Intervention Expedite Internal sources: Interferences Reports External sources: Social Media E-shops Customs Police database Diagnose Prevent Predict Observe

51 Benefits and business values Maggior capacita investigativa. Nuovi tipi di dati. Nuove viste. Calcolo del rischio (es. prevenire vendita equipaggiamenti illegali; fonti social) Maggior Efficacia del Processo Ispettivo. Informazioni quasi real-time. Pianificazione ispezioni piu mirata Dopo Risk Numero Prima casi anomali individuati +20% Costo delle ispezioni -15% Prevenzione (Modern Inspection Agency) Costs Prepare Inspect Intervention Expedite

52 Rossella Oracle Confidential Internal/Restricted/Highly Restricted 52

53 Oracle Big Data Discovery Alessandro Moro BUSINESS ANALYTICS Universita ROMA TRE 21 Maggio 2015 Oracle Confidential Internal/Restricted/Highly Restricted 53

54 The Big Data context on Hadoop Lack of knowledge about Data Not familiar and overwhelming Potential value not obvious Requires significant manipulation Tool Complexity Early Hadoop tools only for experts Existing BI tools not designed for Hadoop Emerging solutions lack broad capabilities 80% effort spent on DATA PREPARATION SCARCE and VERY SPECIALISED resources 54

55 Not Easy to Get Analytic Value from Big Data Today's Challenge Preparation Tomorrow's Goal Preparation Find Explore Discover Transform Analysis Analysis 55

56 Usual approach to Hadoop Information Management find library(gmodels) mydata$ones <- 1 # Create a new variable of ones CrossTable(mydata$Major,digits=2) # Shows horizontal Huge CrossTable(mydata$Major,digits=2, amounts of data are max.width=1) difficult # to Shows access vertical CrossTable(mydata$Major,mydata$onesopro, digits=2) CrossTable(mydata$Gender,mydata$ones, digits=2) Finding CrossTable(mydata$Major,mydata$Gender,digits=2, relevant information is painful. And takes long time. expected=true,dnn=c("major","gender")) CrossTable(mydata$Major,mydata$Gender,digits=2, chisq=true, dnn=c("major","gender")) Example: CrossTable(mydata$Major,mydata$Gender,digits=2, command line, HUE, distribution dnn=c("major","gender")) specific tools,... CrossTable(mydata$Major,mydata$Gender, format=c("spss"), digits=1) chisq.test(mydata$major,mydata$gender) # Null hipothesis: no association # NOTE: Expected value = (row total * column total)/overall total (or total sample size). Value we would expect if all cell were represented proportionally, which indicates no association between variables. This is are we getting what we expect or not. If so then nothing is new. If not then something is going on # NOTE: Chi-sqr = sum (obs-exp)^2/exp Degrees of freedom for Chi-sqr are (r-1)*(c-1) # NOTE: Chi-sqr contribution = (obs-exp)^2/exp # Cramer's V = sqrt(chi-sqr/n*min) Where N is sample size and min is a the minimun of (r-1) or (c-1) 56

57 Usual approach to Hadoop Information Management explore library(gmodels) mydata$ones <- 1 # Create a new variable of ones CrossTable(mydata$Major,digits=2) # Shows horizontal CrossTable(mydata$Major,digits=2, max.width=1) # Shows vertical Exploring requires significant programming effort CrossTable(mydata$Major,mydata$onesopro, digits=2) CrossTable(mydata$Gender,mydata$ones, digits=2) CrossTable(mydata$Major,mydata$Gender,digits=2, expected=true,dnn=c("major","gender")) Example: MapReduce, R, Shark, Mahout, MLlib, Calliope, CrossTable(mydata$Major,mydata$Gender,digits=2, chisq=true, dnn=c("major","gender")) Hive, Pig, Ubercube language skills CrossTable(mydata$Major,mydata$Gender,digits=2, dnn=c("major","gender")) CrossTable(mydata$Major,mydata$Gender, format=c("spss"), digits=1) chisq.test(mydata$major,mydata$gender) # Null hipothesis: no association # NOTE: Expected value = (row total * column total)/overall total (or total sample size). Value we would expect if all cell were represented proportionally, which indicates no association between variables. This is are we getting what we expect or not. If so then nothing is new. If not then something is going on # NOTE: Chi-sqr = sum (obs-exp)^2/exp Degrees of freedom for Chi-sqr are (r-1)*(c-1) # NOTE: Chi-sqr contribution = (obs-exp)^2/exp # Cramer's V = sqrt(chi-sqr/n*min) Where N is sample size and min is a the minimun of (r-1) or (c-1) 57

58 Usual approach to Hadoop Information Management transform Transforming is a key element of the IM cycle Here too significant programming effort is needed Example: Spark, Calliope, Cassandra++ language skills 58

59 Usual approach to Hadoop Information Management discover share Discovery can be done in many different ways and it is where usual BI-Like tools are used, with many limitations OBI, Qlik, SAS, etc. 59

60 Real Life Example of Hadoop Information Management 60

61 A New Approach A single intuitive and visual user interface, to... find explore transform discover share find and explore big data to understand its potential quickly transform and enrich it to make it better unlock big data for anyone to discover and share new value 61

62 Oracle Big Data Discovery The Visual Face of Hadoop Find Explore Discover Transform 62

63 Demo

64 Who are BDD s users? Data Scientists Makes their life a lot easier Influencer but not primary buyer Be careful to not offend Business Analysts and Key Users Use BDD to contribute to Big Data Analytics on Hadoop BDD aids collaboration with Data Scientists Oracle Confidential Internal

65 Oracle Big Data Discovery: key values No data movement to get the value out of Big Data Transform the Hadoop user experience from batch to online find explore transform discover share Rich and advanced visualizations 65

66

67 Oracle Big Data Discovery A Game Changing Platform See the Potential in Big Data, Quickly Make it Better and Unlock Value for Everyone Business Benefits Get value faster. Rapidly turn raw data into actionable insights, leveraged across the enterprise Democratize value from Big Data. Increase the size, diversify the skills, and improve the efficiency of Big Data teams Technical Benefits Destroy existing technical barriers. Run natively on Hadoop cluster for maximum scalability and performance Publish, secure and leverage. Integrate with Hadoop open standards and leverage the unified Oracle big data ecosystem 67

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